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Anthropogenic forcing reduced the probability of rainfall amount in the extended rainy winter of 2018/19 over the middle and lower reaches of the Yangtze River, China, by ~19%, but exerted no influence on the excessive rainy days, based on HadGEM3-GA6-N216 ensembles. Instead the natural variability played a large and important role in this event.

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%.

Persistent night-time heat waves like the event of summer 2018 in Northeast China are extremely rare (about a one-in-a-500-year event) in the natural world, but now have become about a one-in-60-year event with anthropogenic warming.

Changes in intensity, frequency, and location of temperature extreme events are a focus for many studies that often rely on simulations from climate models to assess changes in temperature extremes. Given the use of climate models for attributing such events to human and natural influences and for projecting future changes, an assessment of the capability of climate models to properly simulate the mechanisms associated with temperature extreme events is necessary. In this study, known mechanisms and relevant meteorological variables are explored in a composite analysis to identify and quantify a climatology of synoptic weather patterns related to hot and cold seasonal temperature extreme events over Central Europe. The analysis is based on extremes that recur once or several times per season for better sampling. Weather patterns from a selection of CMIP5 models are compared with patterns derived from the ERA interim reanalysis. The results indicate that climate models simulate mechanisms associated with temperature extreme events reasonably well, in particular circulation-based mechanisms. The amplitude and average length of events is assessed, where in some cases significant deviations from ERA interim are found. In three cases, the models have on average significantly more days per season with extreme events than ERA interim. Quantitative analyses of physical links between extreme temperature and circulation, relative humidity, and radiation reveal that the strength of the link between the temperature and the variables does not vary greatly from model to model and ERA interim.

We show that a systematic discrepancy between model simulations and proxy reconstructions of hemispheric temperature changes over the past millennium appears to arise from a small number of radiatively large volcanic eruptions. Past work has shown that accounting for this mismatch alone appears to reconcile inconsistencies between the overall amplitude of simulated and proxy-reconstructed temperature changes. We provide empirical support for the previously posited hypothesis that this discrepancy may arise from threshold growth effects in tree line-proximal trees that limit their response to large volcanic cooling events. Such threshold responses could lead to an absence of growth rings (as many as six accumulated years over the past eight centuries) for some fraction of tree line-proximal trees, leading to a potential misalignment of volcanic cooling events in trees from climatically distinct regions, and a further attenuation and smearing of the volcanic cooling signal. Since the high-frequency component of nearly all proxy reconstructions of past hemispheric temperature change is derived from tree ring data, this bias would likely impact nearly all such reconstructions. We show here that the discrepancy may have led to an underestimation bias in past studies attempting to infer equilibrium climate sensitivity from proxy temperature reconstructions of the past millennium.

A number of studies have set out to obtain a range of atmosphere and ocean model behavior by perturbing parameters in a single climate model (perturbed physics ensemble: PPE). Early studies used shallow layer slab ocean or flux-adjusted coupled ocean-atmosphere models to obtain a broad range of behavior as characterized by climate sensitivity. A recent study reports a relatively narrow range of sensitivities (2.2–3.2°C) in a PPE of 35 coupled models without flux adjustment, raising the question whether previous broad ranges were an artifact of the use of models that were not in top-of-atmosphere (TOA) energy balance. Moreover, no PPE experiment has reported a large spread of behavior of the ocean compared to that exhibited in a multi-model ensemble (MME) such as Coupled Model Intercomparison Project phase 3 (CMIP3). In this work, we randomly perturb model parameters of a coupled ocean-atmosphere general circulation model using a space-filling design containing 10,000 combinations. The ensemble is run over the distributed computing platform of climateprediction.net under fixed pre-industrial forcing without flux adjustment. We resample a second, 20,000-member, ensemble with perturbations conditioned on the TOA fluxes from the first ensemble to not drift significantly from a realistic base state while targeting a range of behavior. Models within the targeted ensemble show realistic regional control climates when compared to the CMIP3 ensemble, although there is a bias in global mean surface temperature. The range of predicted equilibrium climate sensitivities of the targeted ensemble is substantially smaller than that obtained with flux adjustment, but larger than the range in the CMIP3 ensemble or in the 35-model un-flux-adjusted PPE in a recent study mentioned above. The Atlantic meridional overturning circulation in the targeted ensemble exhibits a spread in strength as wide as that found in the CMIP3 ensemble. We conclude that flux adjustment is not a pre-requisite for obtaining a broad spread of behavior in a perturbed physics ensemble.